Methods for explicitly modeling feature interactions using machine learning have been studied extensively [1]}, [2]}, [3]}, [4]}, [5]}.
Feature interaction methods aim to model high-order interactions through feature crossing, which iteratively multiplies input features, and has shown impressive performance in predictive tasks, such as recommendation [5]} and regression [7]}.
However, there are several problems with the existing feature interaction methods.
First, explicit feature interaction methods are linear.
Therefore, they cannot learn nonlinear functions, and their expressive powers are limited.
Second, most existing feature interaction modeling studies have mainly focused on improving predictive performance.
Although linear models are interpretable, their interpretability tends to be neglected in previous studies.
